Linear Parameter Varying System Identification and application to wind turbines

Europe targets to have renewable energy form 20% of its total energy production by the year 2020, of which 40 GW will be accounted for by off-shore wind turbines. State-of-the-art control of these inherently non-linear wind turbines is done on the basis of interpolated locally linear models. However, these models do not capture all the dominant dynamics of the wind turbine. The limited model fidelity results in conservative control and this in turn limits the performance.

Linear Parameter Varying (LPV) models would be the next step, however there are no efficient algorithms available. Even though state-of-the-art subspace identification methods are numerically reliable, can deal with closed-loop multiple input multiple output systems, and produce models which are very suitable for control design; they result in exponentially scaling parameter counts for LPV identification. This major bottleneck limits application to very small systems and otherwise quickly results in poor model accuracy.

In this project, we will solve the major bottleneck of LPV identification and additionally quantify the fidelity. That is, we will develop a novel, mature LPV subspace identification scheme which is scalable and produces models with quantified high fidelity.